My research focuses on the interplay between computer science and Darwinian
evolution, using the principles of each field to enhance our understanding of
the other. Most of my research makes use of the Avida Digital Evolution software platform,
which implements a population of self-replicating computer programs that evolve in an
open-ended fashion.

Research Interests

(Click on images for more information.)

An annotated representation of all organisms that have ever lived in a population.
The x-axis represents evolutionary time (~17,000 generations).
The y-axis indicates evolutionary distance from the original ancestor (total number of mutations.) Colors
indicate the relative abundance of organisms at a a given distance and time, yellow being more abundant
than red.
The blue line follows the line-of-descent to the final most-abundant genotype in the population. Arrows
indicate the first organism to evolve the complex EQU function and the final most-abundant genotype.

The Evolution of Biological Complexity: A major focus in my research group is understanding how evolution progresses from simple forms to
complex ones. We study this topic from many directions, including understanding the selective pressures that promote complexity, measuring
the flow of information into a genome, and experimenting with different genetic architectures. In all cases, we use digital organisms that allow
for rapid evolution (tens of thousands of generations in a day), the ability to fully control environmental conditions, and complete transparency
in collecting data.

A complex system of task partitioning evolved by a colony of digital organisms. Organisms (squares) export tasks and exchange messages (pairs of arrows) that may include the results of tasks, input values, constants, or previously received messages. Although colonies are limited to 25 organisms at a time, offspring can replace previous organisms; for this case study colony, there are 57 organisms between colony replication events. Each organism sends seven messages and receives one; only successfully received messages are depicted. Organism colors represent tasks exported and thus resources targeted by an organism; black represents organisms that did not export any task. Each message consists of two numbers and is represented by a pair of arrows whose color denotes the contents of the message. Black arrows represent messages that are not the result of a task. Inset highlights four of these organisms: the top organism exporting ORNOT (purple) sends a message containing the solutions to the OR (orange) and ORNOT (purple) tasks to a neighboring organism, which NANDs these results together to export NOT (blue) [i.e., ([A ORNOT B] NAND [A OR B] = NOT A)].

Major Transitions in Evolution: There have been many major evolutionary events that have allowed modern organisms to attain
remarkable levels of complexity. Most of these entail transitions where either individuals come together to form a greater whole (e.g.,
cells in a multi-cellular organism) or entirely new ways of individuals interacting (e.g., sexual recombination).
We use digital evolution systems to examine the environmental pressures that lead to these powerful evolutionary events, with an interest
both in understanding how these events occurred in nature and harnessing them for similarly profound changes in evolved artificial systems.

The Evolution of Intelligence: The origin of natural intelligence is of fundamental interest to both biologists, who are
fascinated by the dynamics that can produce behaviors of such sheer complexity, as well as to engineers who wish to duplicate
these capabilities in computational media. We examine the evolutionary emergence of intelligent responses in digital evolution
systems, with the dual-goals of improving our understanding of evolutionary dynamics and exploiting their constructive potential
to produce robust intelligence in applied systems.

Resources to learn more about my research

Software

The Avida Digital Evolution Platform
is a scientific software package that allows a user to experiment with populations of actively evolving computer programs.
Unlike simulations of evolution, digital organisms in Avida are fully functional computer programs that evolve in an entirely open-ended
fashion, often coming up with unexpected and highly-effective survival strategies.

Avida-ED is an educational version of the Avida software, with a simple and intuitive graphical
user interface. Avida-ED is currently targeted at undergraduate biology courses and is in use at a number of universities throughout the
United States.

Brief Bio: I received my Bachelors of Science from SUNY Stony Brook in 1994, with a triple major in Pure Math, Applied, Math and Computer
Science. I then moved to the graduate program in Computation and Neural Systems at Caltech, where I studied under Dr. Christoph Adami and
Dr. Alan Barr, graduating with my Ph.D. in 1999. Next, I spent three years as a postdoc for Dr. Richard Lenski in the Center for Microbial
Ecology at Michigan State University before getting a faculty position of my own the department of Computer Science and Engineering in 2002.

My multi-disciplinary path began in college where I became fascinated with understanding intelligence and the quest to reproduce it
with computers. The goal of "real AI" has been promised to be only 10-20 years away since the 1940's, but progress in reaching that goal
continues to be slower than expected (though with many remarkable breakthroughs along the way).
As a graduate student, I wanted to learn about neural systems to be better understand how natural brains work, while
performing research on evolution to learn more about the process that produced those brains.
On the surface, evolution is a simple principle, but the details of it are nuanced and it has proven challenging to reproduce its full
potential in a computer -- the entire field of evolutionary computation is focused on this goal.
As such, I spent my time as a postdoc working in a microbial experimental evolution laboratory (though
still focusing on digital evolution work myself) in order to develop a better understanding of the state of the field of evolutionary biology
and the big questions that remained. Since that time, my research has focused more generally on understanding how evolution produces complex
traits and behaviors, with the long-term goal of applying these concepts to the evolution of computational intelligence.

Evolving intelligence is in no way a short-term goal, but I believe that if we are to one day achieve true AI, it will be, at least in part,
due to harnessing those forces that produced intelligence in the natural world.